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Electrical Coupling within Thalamocortical Networks Cumulatively Reduces Cortical Correlation to Sensory Inputs.

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2 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 2 matches
  1. [1] § Materials and Methods ↔ Models.jl/src/TRNnetwork.jl, lines 43–104 · score 0.74 · delayed rectifier, regular sodium, rise, Na, Kd, Kt
  2. [2] § Materials and Methods ↔ Models.jl/src/TRNmodel.jl, lines 52–126 · score 0.72 · delayed rectifier, regular sodium, Kd, Kt, NaT, K2

Paper

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The authors' code

Julia · 153 lines · 4.2 KB · no license · 1 match

  1. @kwdef mutable struct simParams
  2. names::Vector{String}
  3. n::Int
  4. per_neuron::Int
  6. # mS/cm^2
  7. g_caT::Float64 = 0.75
  8. g_nat::Float64 = 60.5
  9. g_kd::Float64 = 60.0
  10. g_nap::Float64 = 0.0
  11. g_kt::Float64 = 5.0
  12. g_k2::Float64 = 0.5
  13. g_ar::Float64 = 0.025
  14. g_GtACR::Float64 = 10.0
  15. g_L::Vector{Float64} = fill(0.1,n)
  17. # mV
  18. E_na::Float64 = 50.0
  19. E_k::Float64 = -100.0
  20. E_ca::Float64 = 125.0
  21. E_ar::Float64 = -40.0
  22. E_L::Float64 = -75.0
  23. E_GtACR::Float64 = -70.0
  24. E_AMPA::Float64 = 0.0
  25. E_GABA::Float64 = -100.0
  27. C::Float64 = 1.0 # membrance capacitance uF/cm^2
  29. # DC pulses # uA/cm^2
  30. bias::Vector{Float64} = zeros(n)
  31. iDC::Vector{Float64} = zeros(n)
  32. iStart::Vector{Vector{Float64}} = fill([0.0],n)
  33. iStop::Vector{Vector{Float64}} = fill([0.0],n)
  35. iexp::Vector{Float64} = zeros(n)
  36. ieStart::Vector{Float64} = zeros(n)
  37. iedecay::Vector{Float64} = fill(30.0,n)
  39. # Silencing
  40. GtACR_on::Vector{Vector{Float64}} = fill([0.0],n)
  41. GtACR_off::Vector{Vector{Float64}} = fill([0.0],n)
  43. # Alpha/Beta Synapses # uA/cm^2
  44. Te1::Float64 = 5.0 #Exc rise time constant
  45. Te2::Float64 = 35.0 #fall time constant
  46. Ti1::Float64 = 5.0 #Inh
  47. Ti2::Float64 = 35.0
  49. A::Vector{Vector{Float64}} = fill([0.0],n)
  50. tA::Vector{Vector{Float64}} = fill([0.0],n)
  52. AI::Vector{Vector{Float64}} = fill([0.0],n)
  53. tAI::Vector{Vector{Float64}} = fill([0.0],n)
  55. # Electrical Synapses # mS/cm^2
  56. gj::Matrix{Float64} = zeros(n,n)
  57. end
  60. function dsim!(du, u, p, t)
  61. for i = 1:p.n
  62. idx = p.per_neuron*(i-1)
  63. v, m_nat, h_nat, m_nap, m_kd, m_kt, h_kt, m_k2, h_k2, m_caT, h_caT, m_ar = u[idx+1:idx+12]
  65. # Inputs
  66. ## Applied current
  67. Iapp = Iapp_f(p.bias[i],p.iDC[i],t,(p.iStart[i],p.iStop[i]))
  68. Iapp += Iexp_f(p.iexp[i],p.iedecay[i],t,p.ieStart[i])
  70. ## External Synapses
  71. ### AMPAergic
  72. A, vpre = ExtSyn_f(t,p.tA[i],p.A[i])
  74. ### GABAergic
  75. AI, vpreI = ExtSyn_f(t,p.tAI[i],p.AI[i])
  77. # Channels
  78. ## Regular sodium
  79. dm_nat, dh_nat = Na_t(v, m_nat, h_nat)
  80. ## Persistent sodium
  81. dm_nap = Na_p(v, m_nap)
  82. ## Delayed rectifier
  83. dm_kd = K_rect(v, m_kd)
  84. ## Transient K = A current, McCormick/Huguenard 1992
  85. dm_kt, dh_kt = K_A(v, m_kt, h_kt)
  86. ## GK2
  87. dm_k2, dh_k2 = K2(v, m_k2, h_k2)
  88. ## T current, as implemented by Traub 2005, which cites Destexhe 1996
  89. dm_caT, dh_caT = Ca_T(v, m_caT, h_caT)
  90. ## Anonymous rectifier, AR; Traub 2005 calls this 'h'. ?!
  91. dm_ar = AR(v, m_ar)
  93. Ina = (p.g_nat*(m_nat^3.0)*h_nat + p.g_nap*m_nap) * (v-p.E_na)
  94. Ik = (p.g_kd*(m_kd^4.0) + p.g_kt*(m_kt^4.0)*h_kt + p.g_k2*m_k2*h_k2) * (v-p.E_k)
  96. ICa = (p.g_caT*(m_caT^2.0)*h_caT) * (v-p.E_ca)
  97. if endswith(p.names[i],"SOM")||endswith(p.names[i],"HO")
  98. ICa *= 0.5
  99. end
  101. IAR = (p.g_ar*m_ar) * (v-p.E_ar)
  102. IL = (p.g_L[i]) * (v-p.E_L)
  104. IGtACR = GtACR_f(p.g_GtACR,t,(p.GtACR_on[i],p.GtACR_off[i])) * (v-p.E_GtACR)
  106. # Synapses
  107. ## Excitatory input
  108. du[idx+13] = p.Te1*K_syn(vpre)*(1.0-u[idx+13]) - p.Te2*u[idx+13]
  109. Esyn1 = A*u[idx+13] * (v-p.E_AMPA)
  111. ## Inhibitory input
  112. du[idx+14] = p.Ti1*K_syn(vpreI)*(1.0-u[idx+14]) - p.Ti2*u[idx+14]
  113. Isyn1 = AI*u[idx+14] * (v-p.E_GABA)
  115. ## Electrical synapses TRN
  116. Gsyn = Gsyn_f(v,u[1:p.per_neuron:end],p.gj[:,i])
  118. Summed_Isyn = Esyn1 + Isyn1 + Gsyn
  120. # Final equations
  121. du[idx+1] = (-1.0/p.C)*(Ina + Ik +ICa + IAR + IL + IGtACR + Iapp + Summed_Isyn)
  122. du[idx+2] = dm_nat
  123. du[idx+3] = dh_nat
  124. du[idx+4] = dm_nap
  125. du[idx+5] = dm_kd
  126. du[idx+6] = dm_kt
  127. du[idx+7] = dh_kt
  128. du[idx+8] = dm_k2
  129. du[idx+9] = dh_k2
  130. du[idx+10] = dm_caT
  131. du[idx+11] = dh_caT
  132. du[idx+12] = dm_ar
  133. end
  135. return nothing
  136. end
  138. function initialconditions(numNeurons, bias = true)
  139. u_init = [-70.0, 0.039, 0.85, 0.1, 0.023, 0.24, 0.21, 0.029, 0.76, 0.043, 0.12, 0.29]
  141. if bias == false
  142. u_init = [-72.8, 0.03, 0.9, 0.076, 0.018, 0.18, 0.3, 0.024, 0.8, 0.03, 0.19, 0.4]
  143. end
  145. u_init = [u_init; zeros(2)]
  146. per_neuron = length(u_init)
  148. u0 = repeat(u_init, numNeurons)
  150. return u0, per_neuron
  151. end

TRNnetwork.jl at commit 98ca09d, no license · at the source

Overview

Authors: Austin J. Mendoza1, Julie S. Haas1
ORCID iDs: Austin J. Mendoza, Julie S. Haas
  1. Department of Biological Sciences, Lehigh University, Bethlehem, Pennsylvania 18015
Institutions: Lehigh University (United States)
Journal: eNeuro, volume 13, issue 6, pages ENEURO.0029-26.2026
Dates: received 29 January 2026; accepted 21 May 2026; published online 12 June 2026; in print June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1523/eneuro.0029-26.2026 · PMID 42230149 · PMCID PMC13271814 · OpenAlex W4413717947
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.08.21.671565
Status: code verified
Methods: Evoked potentials, Single-unit activity, calcium imaging
Keywords: cortex, electrical synapse, gap junction, thalamic reticular nucleus, thalamocortical, thalamus
MeSH: Cerebral Cortex*, Electrical Synapses*, Neurons*, Thalamic Nuclei*, Thalamus*, Action Potentials, Animals, Models, Neurological, Neural Pathways (* major topic)
Journal subjects: Research Article: New Research, Neuronal Excitability
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: HHS | NIH | National Institute of Neurological Disorders and Stroke (NINDS) (NS128713)
Citations: not cited yet (Europe PMC); 81 references in the paper

Abstract

Thalamocortical (TC) cells relay sensory information to the cortex as well as driving their own feedback inhibition through collateral excitation of the thalamic reticular nucleus (TRN). Inhibitory TRN cells are extensively coupled through electrical synapses. While electrical synapses are most often noted for synchronizing rhythmic forms of neuronal activity, their modulation of transient neuronal signals is less understood. Here we sought to characterize how electrical synapses embedded within a network of TRN neurons regulate the processing of ongoing sensory inputs during relay from thalamus to cortex. We constructed a thalamocortical network consisting of reciprocally connected Hodgkin–Huxley-style TC and TRN cells and one cortical output cell summing the TC activity. TRN cells were each electrically coupled to two neighboring cells, forming a ring topology. TC cells received synaptic inputs in sequence, with inputs separated by 10–50 ms, allowing us to assess the functional radius of an electrical synapse by comparing the cumulative effects of each additional TRN electrical synapse on responses within the network. Electrical synapse strength altered both TRN and TC spike response rates and latencies with each additional electrical synapse. Coupling within TRN modulated cortical integration of TC inputs by unexpectedly increasing response rates, duration, and reducing spike correlation to the input sequence that was presented to the TC layer. Thus, embedded TRN electrical synapses exert powerful influence on thalamocortical relay, highlighting the multisynaptic influences of electrically coupled cells on more complex and realistic networks of the brain.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

jhaaslab/RingModel

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 98ca09d0c59885497564791baa94f1b79a9045a7, 16 September 2025
Languages: Julia (22), MATLAB (10)
Size: 257 files, 32 scripts
Software Heritage: not archived
Found in: “Code accessibility”
Holds: README, environment (Manifest.toml, Project.toml, Models.jl/Manifest.toml, Models.jl/Project.toml), documentation
Not found: license file, CITATION.cff, tests, continuous integration
Tools: DifferentialEquations.jl (10 files), Plots.jl (2 files), Makie (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
33 files
At the source: github.com/jhaaslab/RingModel

Code accessibility

The code/software described in the paper is freely available online at https://github.com/jhaaslab/RingModel. The code is available as Extended Data (https://doi.org/10.1523/ENEURO.0029-26.2026.d1).

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 32 scripts, each with its path and the digest of its content;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

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Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

  • Authors: added Austin J. Mendoza (0000-0001-6823-6624); Julie S. Haas (0000-0002-1571-8512); removed Austin J. Mendoza; Julie S. Haas

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 6 keywords, 9 MeSH terms, 1 funder, 80 references.

Cite

This paper

Mendoza, A. J., & Haas, J. S. (2026). Electrical Coupling within Thalamocortical Networks Cumulatively Reduces Cortical Correlation to Sensory Inputs. eNeuro, 13(6), ENEURO.0029-26.2026. https://doi.org/10.1523/eneuro.0029-26.2026

BibTeX

@article{mendoza2026electrical,
author = {Mendoza, Austin J. and Haas, Julie S.},
title = {{Electrical Coupling within Thalamocortical Networks Cumulatively Reduces Cortical Correlation to Sensory Inputs}},
journal = {eNeuro},
year = {2026},
month = jun,
volume = {13},
number = {6},
pages = {ENEURO.0029--26.2026},
publisher = {Society for Neuroscience},
issn = {2373-2822},
doi = {10.1523/eneuro.0029-26.2026},
url = {https://doi.org/10.1523/eneuro.0029-26.2026},
pmid = {42230149},
pmcid = {PMC13271814}
}

RIS

TY - JOUR
AU - Mendoza, Austin J.
AU - Haas, Julie S.
TI - Electrical Coupling within Thalamocortical Networks Cumulatively Reduces Cortical Correlation to Sensory Inputs
T2 - eNeuro
J2 - eNeuro
PY - 2026
DA - 2026/06/16
VL - 13
IS - 6
SP - ENEURO.0029
EP - 26.2026
SN - 2373-2822
PB - Society for Neuroscience
DO - 10.1523/eneuro.0029-26.2026
UR - https://doi.org/10.1523/eneuro.0029-26.2026
LA - en
ER -

CSL-JSON

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